Are you SURE?: Assessing patient decisional conflict with a 4-item screening test.
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability and validity of the 4-item SURE (Sure of myself; Understand information; Risk-benefit ratio; Encouragement) screening test for decisional conflict in patients. DESIGN: Cross-sectional study. SETTING: Four family medicine groups in Quebec and 1 rural academic medical centre in New Hampshire. PARTICIPANTS: One hundred twenty-three French-speaking pregnant women considering prenatal screening for Down syndrome and 1474 English-speaking patients referred to watch condition-specific video decision aids. MAIN OUTCOME MEASURES: Cronbach alpha was used to assess the reliability of SURE. A factorial analysis was performed to assess its unidimensionality. The Pearson correlation coefficient was computed between SURE and the Decisional Conflict Scale to assess concurrent validation. A t test procedure comparing the SURE scores of patients who had made decisions with the scores of those who had not was used to assess construct validation. RESULTS: Among the 123 French-speaking pregnant women, 105 (85%) scored 4 out of 4 (no decisional conflict); 10 (8%) scored 3 (<or= 3 indicates decisional conflict); 7 (6%) scored 2; and 1 (1%) scored 1. Among the 1474 English-speaking treatment-option patients, 981 (67%) scored 4 out of 4; 272 (18%) scored 3; 147 (10%) scored 2; 54 (4%) scored 1; and 20 (1%) scored 0. The reliability of SURE was moderate (Cronbach alpha of 0.54 in French-speaking pregnant women and 0.65 in treatment-option patients). In the group of pregnant women, 2 factors accounted for 72% of the variance. In the treatment-option group, 1 factor accounted for 49% of the variance. In the group of pregnant women, SURE correlated negatively with the Decisional Conflict Scale (r = -0.46; P < .0001); and in the group of treatment-option patients, it discriminated between those who had made a choice for a treatment and those who had not (P < .0001). CONCLUSION: The SURE screening test shows promise for screening for decisional conflict in both French- and English-speaking patients; however, future studies should assess its performance in a broader group of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".